A technology of detecting an object has been widely used with orthogonal splitting (single feature splitting) for a boosted detector in the past. Compared to an oblique splitting method, the orthogonal splitting method has a high calculation efficiency but it has a burden of increasing the number of decision trees to increase object detection rates or object recognition rates for features with high correlation. Accordingly, a technology of decorrelating or whitening features and classifying them by using the orthogonal splitting method is recently drawing attention. Compared to the orthogonal splitting method, the oblique splitting method has a fault of heavy calculation, but it has a high efficiency of classification with respect to highly correlated data when it is used with random forests technology.
However, because both the orthogonal splitting method and the oblique splitting method still require more computations, a method for classifying the object faster and more effectively is required.